Citric Acid Water as an Alternative to Water Restriction for High-Yield Mouse Behavior.

Powerful neural measurement and perturbation tools have positioned mice as an ideal species for probing the neural circuit mechanisms of cognition. Crucial to this success is the ability to motivate animals to perform specific behaviors. One successful strategy is to restrict their water intake, rewarding them with water during a behavioral task. However, water restriction requires rigorous monitoring of animals' health and hydration status and can be challenging for some mice. We present an alternative that allows mice more control over their water intake: free home-cage access to water, made slightly sour by a small amount of citric acid (CA). In a previous study, rats with free access to CA water readily performed a behavioral task for water rewards, although completing fewer trials than under water restriction (Reinagel, 2018). We here extend this approach to mice and confirm its robustness across multiple laboratories. Mice reduced their intake of CA water while maintaining healthy weights. Continuous home-cage access to CA water only subtly impacted their willingness to perform a decision-making task, in which they were rewarded with sweetened water. When free CA water was used instead of water restriction only on weekends, learning and decision-making behavior were unaffected. CA water is thus a promising alternative to water restriction, allowing animals more control over their water intake without interfering with behavioral performance

A standardized and reproducible method to measure decision-making in mice.

Abstract Progress in neuroscience is hindered by poor reproducibility of mouse behavior. Here we show that in a visual decision making task, reproducibility can be achieved by automating the training protocol and by standardizing experimental hardware, software, and procedures. We trained 101 mice in this task across seven laboratories at six different research institutions in three countries, and obtained 3 million mouse choices. In trained mice, variability in behavior between labs was indistinguishable from variability within labs. Psychometric curves showed no significant differences in visual threshold, bias, or lapse rates across labs. Moreover, mice across laboratories adopted similar strategies when stimulus location had asymmetrical probability that changed over time. We provide detailed instructions and open-source tools to set up and implement our method in other laboratories. These results establish a new standard for reproducibility of rodent behavior and provide accessible tools for the study of decision making in mice

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches